Recommender systems are gaining wide popularity in e-commerce as they are becoming major drivers of incremental business value and user satisfaction. Existing recommender systems address recommendations in a stable collection of catalogued items. For example, the recommender system provided by Amazon® recommends products from a stable catalogue of products that do not expire for a long time period. In the case of Netflix® recommendations, recommended movies are selected from a stable cataloged collection. These traditional recommendation systems are based on pre-computing item-item relationships using collaborative filtering methods. Collaborative filtering methods compute an item-item matrix using user behavioral data such as co-purchases or co-views.
However, building a recommendation engine for a large open marketplace (e.g., eBay.com) with dynamic and uncatalogued items may present many challenges. For example, in large open marketplaces such as eBay the majority of listings for items are unstructured and the listings are also short-lived as the items are often purchased within 1-2 weeks of availability. Hence, pre-computing recommendations using traditional techniques like item-item collaborative filtering is not feasible. On the other hand, a solution based on completely online computation is not scalable. Another challenge in an open marketplace setting is that recommendation systems also need to address factors like seller trustworthiness and item quality.